The disclosed embodiments of the present invention relate to generating a depth information related map, and more particularly, to a method and apparatus for generating a final depth information related map (e.g., a final depth map or a final disparity map) that is reconstructed from a coarse depth information related map (e.g., a coarse depth map or a coarse disparity map) through guided interpolation.
A stereo image pair includes two images with disparity. It stimulates human's vision system to get depth information. The disparity is the displacement between two corresponding points in the stereo image pair. If there is a disparity between two points in the stereo image pair, we can recover the depth information from their disparity. Specifically, in human vision system, images taken from the left eye and the right eye are not the same, and the position of the corresponding points in each image is slightly different. The difference is generally called “disparity”. The depth perception is inversely proportional to the disparity. Therefore, when an object has larger disparity in the stereo image pair, the object perceived by the user would have a nearer depth, and when the object has smaller disparity in the stereo image pair, the object perceived by the user would have a furtherer depth.
The generation of a depth information related map, such as a disparity map or a disparity map, is important to a three-dimensional (3D) imaging application. For example, regarding an application of converting a two-dimensional (2D) video into a three-dimensional (3D) video, the depth map may be generated by using one of well-known algorithms, such as computed image depth (CID), bilateral filtering and guided filtering. However, the image details are not kept by the conventional CID approach with block-based operations. As a result, object boundaries are blurred, and the depth perception is reduced. The conventional bilateral filtering approach uses double Gaussian filters to control the blurriness and object boundary. However, it is difficult to adjust the filter parameters to achieve both aggressive smoothing and edge preservation. Besides, it requires large computing power for the double Gaussian filters. Regarding the conventional guided filtering approach, it does not have a linear kernel for controlling the blurriness. As a result, the burden of adjusting the output image characteristic is increased inevitably.
Thus, there is a need for an innovative design which can generate a depth map/disparity map with low computational complexity and enhanced object boundaries.